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| from langchain.chat_models import ChatOpenAI | |
| # from langchain_community.document_loaders import WebBaseLoader | |
| # from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| import gradio as gr | |
| from langchain_pinecone import Pinecone | |
| from langchain_openai import OpenAIEmbeddings , ChatOpenAI | |
| from langchain.prompts import PromptTemplate | |
| from langchain.chains import RetrievalQA | |
| import os | |
| os.environ["PINECONE_API_KEY"] = "9952de06-975b-4ca4-9908-491e8d08328a" | |
| os.environ["OPENAI_API_KEY"]="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" | |
| # create embeddings and store in vector database (Pinecone) | |
| embeddings=OpenAIEmbeddings() | |
| #docsearch=Pinecone.from_texts([t.page_content for t in document_chunks],embeddings,index_name="index_name") | |
| llm = ChatOpenAI(temperature=0, model='gpt-3.5-turbo-16k') | |
| # If you already have embeddings stored , you can load it using below code | |
| vector_search=Pinecone.from_existing_index("erginouswebsite",embeddings) | |
| template = """You are provided with a content related to the company. You will be asked any question related to the company. If you don't know ,just say I don't know the answer'. | |
| {context} | |
| Question: {question} | |
| Helpful Answer:""" | |
| QA_CHAIN_PROMPT = PromptTemplate(input_variables=["question"],template=template) | |
| qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vector_search.as_retriever(),chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}) | |
| def predict(message, history): | |
| gpt_response = qa.invoke(message) | |
| return gpt_response['result'] | |
| gr.ChatInterface(predict,chatbot=gr.Chatbot(height=300), | |
| textbox=gr.Textbox(placeholder="Ask me a question", container=False, scale=7), | |
| title="AI Assistant Chatbot", | |
| description="Chatbot AI Assistant", | |
| theme="soft", | |
| examples=["Provide address of the company", "Solutions provided by the company", "Career opportunities in company"], | |
| cache_examples=True, | |
| retry_btn=None, | |
| undo_btn="Delete Previous", | |
| clear_btn="Clear",).launch(share=True , server_name="0.0.0.0" ,server_port=8080) |